DYNAMIC COMPLEX NETWORK ANALYSIS OF PM2.5 IN HENAN PROVINCE OF CHINA

被引:0
|
作者
Liu, L. [1 ]
Li, H. [1 ]
Li, W. W. [1 ]
Sui, Q. L. [1 ]
Zhu, Y. H. [1 ]
机构
[1] Shandong Univ Sci & Technol, Qingdao, Peoples R China
来源
APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH | 2022年 / 20卷 / 04期
关键词
air pollution; PM2.5; network; cross-correlation analysis; Granger causality test; trophic coherence; SPATIAL-DISTRIBUTION;
D O I
10.15666/aeer/2004_30333056
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
At present, air pollution has become a major environmental problem threatening human health. PM2.5 concentration is an important indicator to measure air pollution. Studying the distribution and interaction of PM2.5 concentration between cities can provide a scientific basis for air quality monitoring, air pollution control, and the formulation of collaborative strategy for economy and environment in Henan Province of China. According to the PM2.5 concentration data of each prefecture-level city in 2018, we analyze the correlation of PM2.5 concentration between cities in Henan Province of China. Further, we construct a directed complex network of PM2.5 interaction based on Granger causality to explore the directivity of the impact between cities in Henan Province of China. Then, we introduce the "trophic coherence" method in biology to infer the hierarchical structure and stability of the network. The research indicates: (1) there are the evident of seasonal differences in PM2.5 concentration in Henan Province of China. The mean of PM2.5 concentration in the four seasons shows different trends, and there is the relatively obvious holiday effect. (2) In different seasons, the cross-correlation of PM2.5 concentration between cities is different. The cross-correlation between cities in spring and summer shows obvious spatial heterogeneity, and PM2.5 concentration between cities in autumn and winter shows higher spatial embeddedness. (3) The impact of PM2.5 concentration between cities in Henan Province of China has obvious causal directivity. The trophic coherence of the PM2.5-directed network is the smallest in autumn, with the most stable structure, while is with the largest vulnerability in summer.
引用
收藏
页码:3033 / 3055
页数:23
相关论文
共 50 条
  • [41] Quantitative anatomy of characteristics and influencing factors of PM2.5 and O3 in Liaoning province of China
    Yang, Hongmei
    Liu, Yanqi
    Jiang, Xiaoqiu
    Gong, Xinqi
    COMMUNICATIONS IN INFORMATION AND SYSTEMS, 2023, 23 (02) : 185 - 212
  • [42] Burden of ischemic heart disease and stroke attributable to exposure to atmospheric PM2.5 in Hubei province China
    Yu, Wenyuan
    Liu, Suyang
    Jiang, Junfeng
    Chen, Gongbo
    Luo, Huijuan
    Fu, Yuanshan
    Xie, Lingling
    Li, Baojing
    Li, Na
    Chen, Shu
    Xiang, Hao
    Tang, Shenglan
    ATMOSPHERIC ENVIRONMENT, 2020, 221
  • [43] Network perspective of embodied PM2.5 - A case study
    Wakeel, Muhammad
    Yang, Siyuan
    Chen, Bin
    Hayat, Tasawar
    Alsaedi, Ahmed
    Ahmad, Bashir
    JOURNAL OF CLEANER PRODUCTION, 2017, 142 : 3322 - 3331
  • [44] PM2.5 Estimation Based on Image Analysis
    Li, Xiaoli
    Zhang, Shan
    Wang, Kang
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2020, 14 (02): : 907 - 923
  • [45] A BIBLIOMETRIC ANALYSIS FOR GLOBAL PM2.5 RESEARCH
    Zhao, Suli
    Hou, Haobo
    Jiao, Limin
    Yang, Tianxue
    FRESENIUS ENVIRONMENTAL BULLETIN, 2016, 25 (12): : 5080 - 5095
  • [46] Weekly cycle of magnetic characteristics of the daily PM2.5 and PM2.5-10 in Beijing, China
    Shi, Meinan
    Wu, Huaichun
    Zhang, Shihong
    Li, Haiyan
    Yang, Tianshui
    Liu, Wei
    Liu, He
    ATMOSPHERIC ENVIRONMENT, 2014, 98 : 357 - 367
  • [47] Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime
    Feng, Shuzhuang
    Jiang, Fei
    Jiang, Ziqiang
    Wang, Hengmao
    Cai, Zhe
    Zhang, Lin
    ATMOSPHERIC ENVIRONMENT, 2018, 187 : 34 - 49
  • [48] Near-surface PM2.5 prediction combining the complex network characterization and graph convolution neural network
    Guyu Zhao
    Hongdou He
    Yifang Huang
    Jiadong Ren
    Neural Computing and Applications, 2021, 33 : 17081 - 17101
  • [49] Near-surface PM2.5 prediction combining the complex network characterization and graph convolution neural network
    Zhao, Guyu
    He, Hongdou
    Huang, Yifang
    Ren, Jiadong
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (24): : 17081 - 17101
  • [50] Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network
    Guo, Qingchun
    He, Zhenfang
    Wang, Zhaosheng
    AEROSOL AND AIR QUALITY RESEARCH, 2023, 23 (06)